How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules
| Source: OpenAI Blog
Tags: OpenAI, Codex, antimicrobial resistance, drug discovery, genome mining, biomedical AI, ChatGPT
César de la Fuente's lab is using OpenAI's Codex and ChatGPT to mine living and extinct genomes for antimicrobial peptides, targeting drug-resistant infections at a time when antimicrobial resistance kills an estimated 700,000 people annually and threatens to become a leading cause of death this century.
Details
Computational biologist César de la Fuente is applying OpenAI's Codex and ChatGPT to antimicrobial resistance (AMR), mining both living genomes and extinct organisms' genetic records for peptide sequences with antimicrobial potential — blending AI bioinformatics with molecular archaeology. Language models allow his lab to scan sequence spaces that would take years through traditional wet-lab methods. Rather than building custom biotech models, they use general-purpose tools — a practical path for academic labs working without large computational budgets. The source is a brief OpenAI Blog case study, not a peer-reviewed publication. Specific outcomes — compounds identified, in vivo results, clinical pipeline status — are not disclosed. Readers wanting primary data should look for de la Fuente's published work in journals such as Nature Biomedical Engineering, where the lab has reported AI-discovered antimicrobial peptides sourced from extinct organisms. The broader signal: established language models are now embedded in early drug discovery workflows, taking roles once reserved for specialized bioinformatics pipelines.